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Observation of Crops by Remote Sensing

 

Introduction

 

Remote sensing is now an important part of our observation and control of the resources of the world. It allows us to see clouds and storms form, the effects of glaciation, the growth of cities, the discovery of mineral and the growth and continuing cycle of the vegetation around us. It helps us to observe and map the change which occur in the world around us due to our actions.

 

The Sensors and Satellites

 

Remote sensing involves equipment measuring the reflected radiation from the earthís surface to planes, balloons or spacecraft. The source of this radiation could either be the sun, the earth itself or radiation sent out from the sensor. Sensors measuring the sunís reflections are limited to daytime, while those with their own source can measure the back scattered radiation at any time. There are also different types of sensors depending on the wavelengths that they are responsive to. The two main types of sensor are optical and microwave (or radar).

SPOT

Wavelength (m m)

TM

Wavelength

Resolution

Band 1

0.50 - 0.59

Band 1

0.458 - 0.526

30

Band 2

0.61 - 0.69

Band 2

0.525 - 0.610

30

Band 3

0.79 - 0.90

Band 3

0.639 - 0.691

30

 

 

Band 4

0.754 - 0.889

30

 

 

Band 5

1.559 - 1.796

30

 

 

Band 6

10.40 - 12.50

120

 

 

Band 7

2.062 - 2.350

30

Optical sensors, which include SPOT (Systeme Pour líObservation de la Terre) and TM (Thematic Mapper) measure within the visible and near-infrared part of the electromagnetic spectrum. TM is described as "a non-photographic imaging system which utilizes an oscillating mirror and seven arrays of detectors which sense electromagnetic radiation in seven different bands". The characteristics of the TM bands were selected to maximise their capabilities for detecting and monitoring different types of Earth's resources, which table 1 shows. The band 6 for TM in table 1 scans thermal (heat) infrared radiation and also has a larger resolution than the rest of the bands.

Optical sensors give good results but are dependant on the sunís illumination for their source of radiation and so only operate during the day. They are also dependant on the weather and require little cloud cover, which is a problem in countries near the equator where storm clouds form every day, and also in areas which are at high altitudes. Sweden, for example had cloud cover of 0/8 - 2/8 for only 15% of the time between April and November in 1988.

 

Microwave sensors or radar can not be affected by cloud cover and emit their own radiation and detect the changes in the reflected radiation, and so these give better classification results than for optical sensors. Radar signals can be transmitted or received in different modes of polarisation, usually the signal is transmitted in one plane perpendicular to the direction of the wave and received in one (either the same or different). However, higher accuracy and classifications can be obtained if we could measure all the polarisations at the same time, since most radiation is unpolarised and vibrates in all directions, this is called Radar polarimetry.

The main satellites using microwave sensors are the ERS-1 (Earth Resources Satellite) by the European Space Agency and the JERS-1 (Japanese Earth Resources Satellite). The ERS-1 is best for measuring water, bare soil and low-level vegetation, while the JERS-1 specialises in forestry and urban areas. Therefore by compiling the data from the two satellites, a clearer picture of the land uses in an area can be obtained. These satellites use SAR (Synthetic Aperture Radar) which operates in the C-band (between 4GHz and 8.5GHz). Another sensor also sensing in the C-band, which is a multi-frequency polarimetric system, the SIR-C, should be able to improve land use classification in the future after successful experiments on board a space shuttle.

 

Remote Sensing and Vegetation

 

The use of a specific area of land can be observed using remote sensing, whether it is an urban area, water, bare, soil, forestry or crops. We can measure how quickly a rainforest is being destroyed, the temperature rise or fall in the oceans and observe the ocean currents. It is also possible to estimate the yield of a crop, the health of it, what type it is, even whether it has just been planted or is ready to be harvested. All this information about the crops is useful for the farmers especially if they have thousands of acres.

 

The Green Leaf

 

To be able to clearly study and differentiate between different crops and other vegetation we must consider the green leaf and what affects the reflectance, absorbance and transmittance of it. The green leaf is built of layers of structural fibrous organic matter that contains pigmented water-filled cells and air spaces. Therefore the three main areas that affect the reflectance, absorbance and transmittance are pigmentation, structure and water content.

Firstly pigment of the leaf contains chlorophyll that absorbs in the visible, so there is a high absorbance and low reflectance and transmittance in the visible. Secondly, the structure which has discontinuities in the refractive indices between the membranes and cytoplasm causing a low absorbance and a medium reflectance and transmittance in the near infrared region (0.7 - 1.3m m). The main differences in reflectance between different plants are due to the thickness of the leaf, which also affects the content of the pigment. So a thin lettuce leaf absorbs little and transmits a lot, however a thick head of wheat absorbs lots and transmits little radiation. Thirdly the amount of water in the leaf is inversely proportional to the reflectance within the near middle infrared range (0.9 - 2.6m m) and accounts for 40% to 80% of the fresh weight of green leaves.

 

Classifications of Different Vegetation

  The greater number of different classifications of the land use that you try to record the less accurate the results will be. If SPOT is used to class the vegetation in a certain area, it needs to be split into four different categories to obtain a high class of accuracy, greater than 90%. These four categories are cereals, roots and vegetables, grassland and woodland. If the results from the TM are used as well the accuracy of discriminating within cereals and root crops is improved. For the case of the TM, each band (table 1) can be used to pick out a certain feature of the land. The soil, water, vegetation and snow or clouds are each recorded within a band and so an image made up of false colours can easily be built.

The differences between the spectral profiles of the crops are affected due to the time of year, the stage of the cropsí growth, and the chemicals in the soils or fertilizers. The four main stages of growth of cereals are tillering (the planting and first shoots), stem extension (vertical growth), heading (formation of the head) and ripening (development of the head as it ripens).

 

Figure 1: The growth stages of cereals

 

At each of theses different stages the crops' reflectance is different and so it is possible to determine the stage at which the crop is in. Although all cereals go through these stages, they take different time periods for each stage. This means that at some time of the year, the crops will be at different stages and hence it will be easier to tell which crop is which. At this point the data will be more accurate than when the stages are identical. For example, one study showed that on the 7 June rye alone is at the heading stage while on 9 July both rye and winter wheat were at the ripening stage. This means the spectral differences between rye and winter wheat is less on 9 July.

Another factor in the reflectance of crops is the soil that they are grown in. In East Anglia, (figure2) the reflectance of winter wheat grown in lighter mineral soils where found to have slightly greater reflectance than wheat grown in darker peat soils.

Disease and pests are a problem for farmers and it can be difficult to detect until it is too late. However since the water content of the leaves affects the reflectance and disease or pests cause the lack of water, the health of the plant can be observed. This means that spraying of crops can be limited to just the infected areas which is more cost efficient. Since satellites can observe diseases and pests quicker than the human eye, the infected areas of the fields can be sprayed before the infection spreads. Also the levels of certain chemicals within the soils can be measured so that, if for example there is a shortage of nitrogen in one area, nitrate fertilizer can be applied with maximum effectiveness.

Conclusions

 

Remote sensing can now be used for the observation of the crop, its growth, health and type can all be measured by sensors in satellites. The sensors can either work in the visible and near infrared or the microwave part of the spectrum, most of the data gathered in the past has been using the optical sensors, though the microwave region is now much more common as it is not dependant on the weather. Each crop reflects radiation in a different way because of its thickness, structure and amount of water. Since crops all grow at different speeds, at some times of the year it is easier to tell the difference between crops, which can be similar if at the same growth stage. Disease, pests or lack of chemicals all affect the reflectance by varying the amount of water or chemicals in the plant and so causing the reflectance to change. In the future, a farmer will be able to monitor and control his crops from the comfort of his own armchair, thanks to remote sensing.

 

References

 

CURRAN, P.J. 1985.

Principles of Remote Sensing, Longman Inc., New York

ELVIDGE, C.D. 1990

Visible and near infrared reflectance characteristics of dry plant materials.

International Journal of Remote Sensing, Vol. 11, No. 10, 1775 - 1795

 

HALL-KÖNYVES, K. 1990

Crop Monitoring in Sweden, International Journal of Remote Sensing,

Vol. 11, No. 3, 461 - 484

 

JEWELL, N. 1989

An evaluation of multi-date SPOT data for agriculture and land use mapping in the United Kingdom, International Journal of Remote Sensing

Vol. 10, No. 6, 939 - 951

 

LILLESAND, T.M., KIEFER, R.W. 1994

Remote Sensing and Image Interpretation (3rd Edition)

John Wiley and Sons, Inc. New York

MARACCI, G., AIFADOPOULOU, D. 1990

Multi-temporal remote sensing study of spectral signatures of crops in the Tessaloniki test site, International Journal of Remote Sensing, Vol. 11, No. 9, 1609-1615